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A Computerized System to Assess Axillary Lymph Node Malignancy from Sonographic Images

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The usefulness of a computer-aided diagnosis scheme for improving the performance of clinicians to diagnose non-mass lesions on breast ultrasonographic images

Shibusawa, M., Nakayama, R., Okanami, Y., Kashikura, Y., Imai, N., Nakamura, T., Kimura, H., Yamashita, M., Hanamura, N., & Ogawa, T. (2016) · Journal of Medical Ultrasonics · doi:10.1007/s10396-016-0718-9 · Cited by 24

Standalone computer-aided detection compared to radiologists’ performance for the detection of mammographic masses

Hupse, R., Samulski, M. R. M., Lobbes, M. B. I., den Heeten, A., Tas, M. W., Beijerinck, D., Pijnappel, R. M., Boetes, C., & Karssemeijer, N. (2012) · European Radiology · doi:10.1007/s00330-012-2562-7 · Cited by 23

2015 27th International Conference on Microelectronics (ICM)

(2015) · doi:10.1109/icm37423.2015 · Cited by 22

Breast abnormality detection in mammograms using Artificial Neural Network

Mina, L. M., & Isa, N. A. M. (2015) · International Conference on Computer, Communications, and Control Technology (I4CT) · doi:10.1109/i4ct.2015.7219577 · Cited by 20

A new hybrid method combining genetic algorithm and support vector machine classifier: Application to CAD system for mammogram images

Azizi, N., Zemmal, N., Sellami, M., & Farah, N. (2014) · doi:10.1109/icmcs.2014.6911285 · Cited by 20

An improved selective attention model considering orientation preferences

Qu, J., Wang, R., & Du, Y. (2011) · Neural Computing and Applications · doi:10.1007/s00521-011-0679-2 · Cited by 19

Robust automatic breast cancer staging using a combination of functional genomics and image-omics

Su, H., Shen, Y., Xing, F., Qi, X., Hirshfield, K. M., Yang, L., & Foran, D. J. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7320059 · Cited by 19

Quantitative ultrasound image analysis of axillary lymph node status in breast cancer patients

Drukker, K., Giger, M. L., Meinel, L. A., Starkey, A., Janardanan, J., & Abé, H. (2013) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-013-0829-3 · Cited by 18

Consistent performance measurement of a system to detect masses in mammograms based on blind feature extraction

García‐Manso, A., García-Orellana, C. J., González–Velasco, H. M., Gallardo–Caballero, R., & Macías, M. M. (2013) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-12-2 · Cited by 18

CAD system for classification of mammographic abnormalities using transductive semi supervised learning algorithm and heterogeneous features

Zemmal, N., Azizi, N., & Sellami, M. (2015) · International Symposium on Programming and Systems (ISPS) · doi:10.1109/isps.2015.7244993 · Cited by 17

Towards localization of malignant sites of asymmetry across bilateral mammograms

Casti, P., Mencattini, A., Salmeri, M., Ancona, A., Lorusso, M., Pepe, M. L., Natale, C. D., & Martinelli, E. (2016) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.11.010 · Cited by 14

New developments in the diagnostic procedures to reduce prospective biopsies breast

Ayed, N. G. B., Masmoudi, A. D., Sellami, D., & Abid, R. (2015) · International Conference on Advances in Biomedical Engineering (ICABME) · doi:10.1109/icabme.2015.7323288 · Cited by 12

Feasibility Testing: Three-dimensional Tumor Mapping in Different Orientations of Automated Breast Ultrasound

Lo, C., Chan, S., Yang, Y., et al. (2016) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2015.12.006 · Cited by 11

Multiple-instance learning for breast cancer detection in mammograms

de la Rosa, R. S., Lamard, M., Cazuguel, G., et al. (2015) · doi:10.1109/embc.2015.7320017 · Cited by 11

2015 International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering (ICCNEEE)

(2015) · doi:10.1109/iccneee34545.2015 · Cited by 11

Statistical block-based DWT features for digital mammograms classification

Berraho, S., Margae, S. E., Kerroum, M. A., & Fakhri, Y. (2014) · International Conference on Intelligent Systems: Theories and Applications · doi:10.1109/sita.2014.6847307 · Cited by 10

A CAD System for the Acquisition and Classification of Breast TMA in Pathology.

Fernández-Carrobles, M. M., Bueno, G., Déniz, Ó., et al. (2015) · PubMed · Cited by 10

A comparison of multi-resolution and multi-orientation for breast cancer diagnosis in the full-field digital mammogram

Addioui, A., Benabbou, F., Filali, S. E., & Aroussi, M. E. (2015) · doi:10.1109/icm.2015.7438037 · Cited by 5

Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review

Houssein, E. H., Emam, M. M., Ali, A. A., & Suganthan, P. N. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.114161 · Cited by 404

Deep learning-based breast cancer classification through medical imaging modalities: State of the art and research challenges

Murtaza, G., Shuib, L., Abdul Wahab, A. W., Mujtaba, G., Mujtaba, G., Nweke, H. F., Al-garadi, M. A., Zulfiqar, F., Raza, G., & Azmi, N. A. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09716-5 · Cited by 342

Evaluation of deep learning detection and classification towards computer-aided diagnosis of breast lesions in digital X-ray mammograms

Al-antari, M. A., Han, S.-M., & Kim, T.-S. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105584 · Cited by 264

Prospective assessment of breast cancer risk from multimodal multiview ultrasound images via clinically applicable deep learning

Qian, X., Pei, J., Zheng, H., Xie, X., Yan, L., Zhang, H., Han, C., Gao, X., Zhang, H., Zheng, W., et al. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00711-2 · Cited by 253

Deep Learning for Smart Healthcare—A Survey on Brain Tumor Detection from Medical Imaging

Arabahmadi, M., Farahbakhsh, R., & Rezazadeh, J. (2022) · Sensors · doi:10.3390/s22051960 · Cited by 253